Explore Our Courses

Machine Learning with Python – 2 Days

 14 時間
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
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Machine Learning with Python – 4 Days

 28 時間
The aim of this course is to provide general proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
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Introduction to Data Science and AI using Python

 35 時間
This is a 5 day introduction to Data Science and Artificial Intelligence (AI). The course is delivered with examples and exercises using Python 
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Applied AI from Scratch in Python

 28 時間
This is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course. 
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BDD with Python and Behave

 7 時間
Behave is an open-source, Python-based BDD framework for writing tests in a natural language style. BDD, or Behavior Driven Development, is an agile software development technique that encourages collaboration among developers, QA and non-technical business people in a software project. This instructor-led, live training begins with a discussion of BDD and how the Behave framework can be used to carry out BDD testing for web applications. Participants are given ample opportunity to interact with the instructor and peers while implementing the concepts and tactics learned in this hands-on, practice-based lab environment. By the end of this training, participants will have a firm understanding of BDD and Behave, as well as the necessary practice to implement these techniques and tools in real-world test scenarios.
Format of the Course
  • Heavy emphasis on hands-on practice. Most of the concepts are learned through samples, exercises and hands-on development.
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Learn Object-Oriented Programming with Python

 14 時間
Object-Oriented Programming (OOP) is a programming paradigm based around the concept of objects. OOP is more data-focused rather than logic-focused. Python is a high-level programming language famous for its clear syntax and code readibility. In this instructor-led, live training, participants will learn how to get started with Object-Oriented Programming using Python. By the end of this training, participants will be able to:
  • Understand the fundamental concepts of Object-Oriented Programming
  • Understand the OOP syntax in Python
  • Write their own object-oriented program in Python
Audience
  • Beginners who would like to learn about Object-Oriented Programming
  • Developers interested in learning OOP in Python
  • Python programmers interested in learning OOP
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Deep Learning for Telecom (with Python)

 28 時間
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to:
  • Understand the fundamental concepts of deep learning.
  • Learn the applications and uses of deep learning in telecom.
  • Use Python, Keras, and TensorFlow to create deep learning models for telecom.
  • Build their own deep learning customer churn prediction model using Python.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Algorithmic Trading with Python and R

 14 時間
Algorithmic trading is the practice of implementing pre-programmed instructions for placing trades. In theory, with algorithmic trading users will be able to achieve profits at a frequency not possible for a human trader. This instructor-led, live training (online or onsite) is aimed at business analysts who wish to automate trade with algorithmic trading, Python, and R. By the end of this training, participants will be able to:
  • Employ algorithms to buy and sell securities at specialized increments rapidly.
  • Reduce costs associated with trade using algorithmic trading.
  • Automatically monitor stock prices and place trades.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Anomaly Detection with Python and R

 14 時間
Anomaly detection is the identification of extreme values that deviate from an overall pattern on a data set. Using Python and R in SAS, users can implement anomaly detection algorithms for identifying anomalies in big data. This instructor-led, live training (online or onsite) is aimed at data scientists and data analysts who wish to program in R and Python in SAS to carry out anomaly detection. By the end of this training, participants will be able to:
  • Identify whether data is an anomaly or is an expected value.
  • Implement algorithms for anomaly detection.
  • Use various techniques and methods to detect anomalies.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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ArcGIS with Python Scripting

 14 時間
ArcGIS* is a geographic information system for working with geospatial data. Using Python with ArcGIS users are able to automate repetitive tasks involved in GIS data administration and analysis. This instructor-led, live training (online or onsite) is aimed at GIS analysts who wish to automate repetitive tasks in GIS processes. By the end of this training, participants will be able to:
  • Build GIS applications using Python and ArcGIS tools.
  • Develop with the ArcGIS package ArcPy, using Python.
  • Apply the ArcGIS modules for map automation using object classes in Python.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Bioinformatics with Biopython

 14 時間
Biopython is an open source library for computational biology and bioinformatics. Biopython provides users the ability to automate medical research in the field of bioinformatics. This instructor-led, live training (online or onsite) is aimed at biologists who wish to use Biopython. By the end of this training, participants will be able to:
  • Integrate biological concepts with information technologies to solve research problems.
  • Mine information from large datasets of biological origin.
  • Create algorithms for essential research tasks.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Building Chatbots in Python

 21 時間
ChatBots are computer programs that automatically simulate human responses via chat interfaces. ChatBots help organizations maximize their operations efficiency by providing easier and faster options for their user interactions. In this instructor-led, live training, participants will learn how to build chatbots in Python. By the end of this training, participants will be able to:
  • Understand the fundamentals of building chatbots
  • Build, test, deploy, and troubleshoot various chatbots using Python
Audience
  • Developers
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
Note
  • To request a customized training for this course, please contact us to arrange.
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Continuous Integration / Continuous Delivery (CI/CD) with Python

 14 時間
Continuous Integration / Continuous Delivery (CI/CD) is a software development practice where application development and deployment are done more frequently in smaller increments. It drives seamless integration of code changes, and automation in building, testing, and deploying applications. This instructor-led, live training (online or onsite) is aimed at developers or DevOps engineers who wish to build automation pipelines using CI/CD practices with Python. By the end of this training, participants will be able to:
  • Set up the necessary development environment to start building CI/CD pipelines with Python.
  • Build automated pipelines for testing and publishing Python packages using Travis-CI.
  • Automate the deployment of containerized applications with Docker and Heroku.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course (e.g, Heroku or Travis-CI alternatives), please contact us to arrange.
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GPU Programming with CUDA and Python

 14 時間
CUDA (Compute Unified Device Architecture) is a parallel computing platform and API created by Nvidia. This instructor-led, live training (online or onsite) is aimed at developers who wish to use CUDA to build Python applications that run in parallel on NVIDIA GPUs. By the end of this training, participants will be able to:
  • Use the Numba compiler to accelerate Python applications running on NVIDIA GPUs.
  • Create, compile and launch custom CUDA kernels.
  • Manage GPU memory.
  • Convert a CPU based application into a GPU-accelerated application.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Data Mining with Python

 14 時間
This instructor-led, live training (online or onsite) is aimed at data analysts and data scientists who wish to implement more advanced data analytics techniques for data mining using Python. By the end of this training, participants will be able to:
  • Understand important areas of data mining, including association rule mining, text sentiment analysis, automatic text summarization, and data anomaly detection.
  • Compare and implement various strategies for solving real-world data mining problems.
  • Understand and interpret the results. 
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Deep Learning for Banking (with Python)

 28 時間
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to:
  • Understand the fundamental concepts of deep learning
  • Learn the applications and uses of deep learning in banking
  • Use Python, Keras, and TensorFlow to create deep learning models for banking
  • Build their own deep learning credit risk model using Python
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Deep Learning for Finance (with Python)

 28 時間
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to:
  • Understand the fundamental concepts of deep learning
  • Learn the applications and uses of deep learning in finance
  • Use Python, Keras, and TensorFlow to create deep learning models for finance
  • Build their own deep learning stock price prediction model using Python
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Fraud Detection with Python and TensorFlow

 14 時間
TensorFlow is an open source machine learning library. TensorFlow provides users the ability to use and create artificial intelligence for detecting and predicting fraud. This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use TensorFlow to analyze potential fraud data. By the end of this training, participants will be able to:
  • Create a fraud detection model in Python and TensorFlow.
  • Build linear regressions and linear regression models to predict fraud.
  • Develop an end-to-end AI application for analyzing fraud data.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Fundamentals of Artificial Intelligence (AI) and Machine Learning

 28 時間
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to:
  • Understand the fundamental concepts of deep learning.
  • Learn the applications and uses of deep learning in telecom.
  • Use Python, Keras, and TensorFlow to create deep learning models for telecom.
  • Build their own deep learning customer churn prediction model using Python.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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GANs and Variational Autoencoders in Python

 14 時間
GANs (Generative Adversial Networks) are a class of neural networks that produce new data that resembles training data. A Variational autoencoder is a neural network architecture that learns the best encoding-decoding scheme for dimensionality reduction. Variational autoencoders can improve GANs through dimensional iteration. This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use GANs and variational autoencoders to generate new, synthetic instances of images, videos, and audio. By the end of this training, participants will be able to:
  • Build a GAN using machine learning libraries in Python.
  • Create a variational autoencoder with TensorFlow.
  • Improve the training stability of GANs.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Deploying Python Web Applications with Gunicorn

 7 時間
Gunicorn (Green Unicorn) is a Python WSGI (Web Server Gateway Interface) HTTP Server that forwards requests from a web server (Apache, NGINX, etc.) to a Python web application or framework in the backend. This instructor-led, live training (online or onsite) is aimed at engineers who wish to deploy a speedy, resource-light Gunicorn server to run Python web applications. By the end of this training, participants will be able to:
  • Install and configure Gunicorn.
  • Understand the building blocks for deploying a Python web application
  • Explain Gunicorn's role and how it compares to Java's Servlet API.
  • Integrate Gunicorn with a variety of Python web frameworks.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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IoT Programming with Python

 14 時間
Internet of Things (IoT) is a network infrastructure that connects physical objects and software applications wirelessly, allowing them to communicate with each other and exchange data via network communications, cloud computing, and data capture. Python is a high-level programming language recommended for IoT due to its clear syntax and large community support. In this instructor-led, live training, participants will learn how to program IoT solutions with Python. By the end of this training, participants will be able to:
  • Understand the fundamentals of IoT architecture
  • Learn the basics of using Raspberry Pi
  • Install and configure Python on Raspberry Pi
  • Learn the benefits of using Python in programming IoT systems
  • Build, test, deploy, and troubleshoot an IoT system using Python and Raspberry Pi
Audience
  • Developers
  • Engineers
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
Note
  • To request a customized training for this course, please contact us to arrange.
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Apache Kafka for Python Programmers

 7 時間
Apache Kafka is an open-source stream-processing platform that provides a fast, reliable, and low-latency platform for handling real-time data analytics. Apache Kafka can be integrated with available programming languages such as Python. This instructor-led, live training (online or onsite) is aimed at data engineers, data scientists, and programmers who wish to use Apache Kafka features in data streaming with Python. By the end of this training, participants will be able to use Apache Kafka to monitor and manage conditions in continuous data streams using Python programming. Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Advanced Deep Learning with Keras and Python

 14 時間
Keras is an open source Python neural-network library for the creation of deep learning neural-networks. Keras offers an intuitive set of abstractions, simplifying the development of deep learning neural-networks and models. This instructor-led, live training (online or onsite) is aimed at software engineers who wish to develop advanced deep learning neural-networks and model using Keras and Python. By the end of this training, participants will be able to:
  • Apply deep learning with supervised or unsupervised learning methods.
  • Develop, train, and implement concurrent neural networks and recurrent neural networks.
  • Use Keras and Python to build deep learning models to solve problems involving images, text, sound, and more.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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KNIME with Python and R for Machine Learning

 14 時間
KNIME is an open source data analytical software for integrating machine learning and data mining through data pipelines. With Python and R, users are able to extend KNIME in its capabilities for data analytics and machine learning. This instructor-led, live training (online or onsite) is aimed at data scientists who wish to program in Python and R for KNIME. By the end of this training, participants will be able to:
  • Plan, build, and deploy machine learning models in KNIME.
  • Implement end to end data science projects.
  • Make data driven decisions for operations.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Building Microservices with Python

 7 時間
Microservices refer to an application architecture style that promotes the use of independent, self-contained programs. Python is a dynamic high-level programming language that is ideal for both scripting as welll as application development. Python's expansive library of open source tools and frameworks make it a practical choice for building microservices. In this instructor-led, live training, participants will learn the fundamentals of microservices as they step through the creation of a microservice using Python. By the end of this training, participants will be able to:
  • Understand the basics of building microservices
  • Learn how to use Python to build microservices
  • Learn how to use Docker to deploy Python based microservices
Audience
  • Developers
  • Programmers
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Machine Learning for Banking (with Python)

 21 時間
Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Machine Learning for Finance (with Python)

 21 時間
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. By the end of this training, participants will be able to:
  • Understand the fundamental concepts in machine learning
  • Learn the applications and uses of machine learning in finance
  • Develop their own algorithmic trading strategy using machine learning with Python
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Python for Network Engineers

 14 時間
Networking is the maintenance, management, and design for computer networks that support wireless network services. Incorporating Python into the networking environment enables the use of key libraries and modules for quick and easy automation. This instructor-led, live training (online or onsite) is aimed at network engineers who wish to maintain, manage, and design computer networks with Python. By the end of this training, participants will be able to:
  • Optimize and leverage Paramiko, Netmiko, Napalm, Telnet, and pyntc for network automation with Python.
  • Master multi-threading and multiprocessing in network automation.
  • Use GNS3 and Python for network programming.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Python for Natural Language Generation

 21 時間
Natural language generation (NLG) refers to the production of natural language text or speech by a computer. In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content. By the end of this training, participants will be able to:
  • Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting
  • Select and organize source content, plan sentences, and prepare a system for automatic generation of original content
  • Understand the NLG pipeline and apply the right techniques at each stage
  • Understand the architecture of a Natural Language Generation (NLG) system
  • Implement the most suitable algorithms and models for analysis and ordering
  • Pull data from publicly available data sources as well as curated databases to use as material for generated text
  • Replace manual and laborious writing processes with computer-generated, automated content creation
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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お客様の声(25)

コースは単純明快で直観的で理解しやすく、演習では事前に議論された主題が取り上げられました。

Alexandru - BRD
コース - Python Programming Fundamentals

Machine Translated

様々なケースの例や資料がたくさんあり、今後、レッスンを仕事に応用する際に役立つと思います。また、レッスンとレッスンの間に自宅でできる課題があるのも、前回のレッスンで苦手だった部分を補う機会になって良かったです。レッスン中にわからないことがあれば、先生が快く教えてくれたり、説明してくれたり、どんな質問にも専門的な知識を持って対応してくれました。

Raivis - Gravity Team
コース - Python: Automate the Boring Stuff

Machine Translated

トレーナーはこの件についてよく理解していることを示した。

Marino - EQUS - The University of Queensland
コース - Machine Learning with Python – 2 Days

Machine Translated

Python を使用したデータ分析に関しては非常に包括的です

Mervin Lau - MINDEF
コース - Python for Data Analysis

Machine Translated

全てにおいて、素晴らしいトレーナーです。

Michał Rawicki
コース - Unit Testing with Python

Machine Translated

さまざまな例が非常にインタラクティブで、トレーニングの開始から終了までの間に複雑さが徐々に増していきます。

Jenny - Andheo
コース - GPU Programming with CUDA and Python

Machine Translated

知識、実質的な内容、他者との接触、些細な問題でも喜んで手助けする姿勢、聞き手の興味を引く能力。

Michał
コース - Python for Excel

Machine Translated

例と演習

Kamil
コース - Introduction to Data Science and AI using Python

Machine Translated

興味深い知識

Gabriel - MINDEF
コース - Machine Learning with Python – 4 Days

Machine Translated

クライアントへのアプローチの柔軟性。トレーナーは、トレーニング参加者が興味を持つ問題を準備することができました。

Mirosław - CREDIT SUISSE (POLAND)
コース - Python Programming - 4 days

Machine Translated

実用的な例とより幅広い背景が示されています。

James - Mitsubishi Electric R&D Centre Europe BV (MERCE-UK)
コース - IoT Programming with Python

Machine Translated

I liked that it was practical. Loved to apply the theoretical knowledge with practical examples.

Aurelia-Adriana - Allianz Services Romania
コース - Python and Spark for Big Data (PySpark)

examples based on our data

Witold - P4 Sp. z o.o.
コース - Deep Learning for Telecom (with Python)

The fact that we were able to take with us most of the information/course/presentation/exercises done, so that we can look over them and perhaps redo what we didint understand first time or improve what we already did.

Raul Mihail Rat - Accenture Industrial SS
コース - Python, Spark, and Hadoop for Big Data

I'm looking forward to having a training again with Any, he was really good, I'm just a GIS guy, but Any made everything clear to me, he explained complex processes in layman's terms. Keep it up, thank you.

Lwazi Qhingana - South African National Roads Agency (SANRAL) SOC Ltd
コース - Python for Geographic Information System (GIS)

Plenty of examples - and the trainer willing to bend backwards to help us with topics we were weaker in.

Wei Lit Teoh - HP Singapore (Private) Ltd.
コース - Advanced Python - 4 Days

トレーナーの親しみやすさとコミュニケーション能力は非常に効果的です。

Ciprian Ilie - Institutul National de Sanatate Publica
コース - Programming for Biologists

Machine Translated

Content and example. virtual computer is helpful (my version of anaconda does not have Dash library yet)

Jennifer Ni - AllianceBernstein
コース - Python with Plotly and Dash

Many different examples and topics has been covered, from basic investigation to login management and dynamic page management.

Daniele Tagliaferro - Creditsafe Italia Srl
コース - Web Scraping with Python

That it was applying real company data. Trainer had a very good approach by making trainees participate and compete

Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
コース - Applied AI from Scratch in Python

Trainer was very knowlegable and very open to feedback on what pace to go through the content and the topics we covered. I gained alot from the training and feel like I now have a good grasp of image manipulation and some techniques for building a good training set for an image classification problem.

Anthea King - WesCEF
コース - Computer Vision with Python

Trainer was accommodative. And actually quite encouraging for me to take up the course.

Grace Goh - DBS Bank Ltd
コース - Python in Data Science

I genuinely enjoyed the lots of labs and practices.

Vivian Feng - Destination Canada
コース - Data Analysis with SQL, Python and Spotfire

In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.

Sacha Nandlall
コース - Python for Advanced Machine Learning

I did like the exercises.

Office for National Statistics
コース - Natural Language Processing with Python

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Python コンサルティング
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